We combine geometry, mechanics, 3D imaging, stochastic dynamics, biological signaling, and data-driven modeling to understand morphogenesis and develop principles for measuring, predicting, and steering shape.
Our work is organized around three connected themes: Morphogenesis, Morphometry, and Morphorythms.
We study morphogenesis as the formation and transformation of shape in living systems. Our goal is to understand how growth, mechanics, geometry, and biological signaling interact to produce reproducible forms despite noise, perturbations, and changing environments.
We develop data-driven ways to measure biological form and shape change during development. Using 3D imaging, geometry, registration, and computational modeling, we turn growing plant shapes into quantitative variables that can be compared, analyzed, and predicted.
We study how growing systems can be steered toward desired shapes using integrated sensing, feedback, and physical cues. By combining observation, prediction, and control, we aim to understand how shape can be steered along desired developmental trajectories.
Selected directions where we connect plant morphogenesis, quantitative shape analysis, sensing, and feedback.
We study how plant organs regulate shape through growth, mechanics, geometry, biological signaling, and feedback. The goal is to understand the principles by which reproducible forms emerge despite noise, perturbations, and changing environments.
We investigate how root architecture develops in transparent-soil and rhizobox systems, and how below-ground form correlates with shoot growth and plant performance over time. This project connects root morphogenesis, shoot morphogenesis, 3D imaging, and plant–soil sensing.
We develop data-driven models that learn how plant shape changes over time and use those models to predict and guide future growth. This project builds on principles learned from plant morphogenesis and uses tools such as sparse model discovery, state estimation, data assimilation, and feedback control.